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Recently introduced high-accuracy RGB-D cameras are capable of providing high quality three-dimension information (color and depth information) easily. The overall shape of the object can be understood by acquiring depth information. However, conventional methods adopted this camera use depth information only to extract the local feature. To improve the object recognition accuracy, in our approach,...
With the development of the moment technology, the moment descriptors have been used in many applications in the field of object recognition. In recent years, discrete orthogonal moments have been studied and proposed in moment applications as features to characterize image shapes. In this paper, a set of Racah moment based feature vectors is proposed for a Chinese character recognition system. The...
This paper presents a novel shape representation approach, Finite Element Skeleton (FES), based on studies of planar shape analysis from the perspective of Finite Element Method (FEM). First, we derive the shape contour of an object in image with edge detection approach. Then, the approach builds connections of all pixels on the shape contour with the inner center point of the shape, and models these...
The iris of a human is not only relevant for biometry; it is also relevant for the prediction and diagnosis of human health. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors which play an important role for the prevention and treatment of illnesses. Up-to-date the iris diagnosis is done manually and is concerned...
3D object recognition is a fundamental research topic. However, shape only feature descriptors for 3D object recognition have been the main focus of research. With the availability of low cost range plus color sensors, color based descriptors have attracted increasing attention lately. In this paper we present novel 3D object recognition algorithms which use not only shape but also color cues. We...
The robust perception of robots is strongly needed to handle various objects skillfully. In this paper, we propose a novel approach to recognize objects and estimate their 6-DOF pose using 3D feature descriptors, called Geometric and Photometric Local Feature (GPLF). The proposed descriptors use both the geometric and photometric information of 3D point clouds from RGB-D camera and integrate those...
In this study, we address the issue on multilevel object recognition. The multilevel object recognition is object recognition in various levels, that is, simultaneous recognition of its instance, category, material, etc. At each level, many recognition methods have been proposed in the literature. Therefore it is straightforward to design a multilevel object recognition system using conventional methods...
A model-based method for transformation-invariant area descriptor extraction is proposed in this paper in the context of object recognition and image matching. Local image descriptors are extracted in salient circular fragments of variable size, which indicate image locations with high intensity contrast, regional homogeneity and shape saliency. Three different types of descriptors — pose, intensity,...
In this paper, we present a novel object matching approach using the method considering both the similarity on regions and structure in its feature space. The previous works [1], [2] and [3] show that it's possible to formulate the object matching problem as a linear programming problem. However, it remains an open problem how to better use the feature similarity and structure similarity at a same...
Bag-of-Words (BOW) models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching technique is the most notable. In this work, we propose three novel techniques to capture more refined spatial information between image features than that provided...
We propose a novel shape descriptor-Included Angle Histogram -for correspondence recovery of graphic vertex and shape-based object recognition. After detecting points local maximal curvature with and the center point of the contour, we construct vectors from the center point to the curvature points. Consequently the point descriptor can be obtained through computing the histograms of included-angles...
The raster-vector conversion of remote sensing image is a very important task in the extraction and updating of linear objects in cartographic processes. In this paper we present a vectorization method, based on constrained Delaunay triangulation, for line extraction. The constraints are provided by a preprocessing step insuring that these edges belong to line structures in the original image. The...
Image encoding using interest points is a common technique in computer vision. In this paper we present a scale and rotation invariant shape centered interest point (SCIP) detector. By means of detecting singularities in Gradient Vector Flow (GVF) fields we find points of high symmetry in the image. Due to the nature of the underlying GVF field we can employ our features to group together edge-based...
In this paper we propose a method of high-speed 3D object recognition using linear subspace method and our 3D features. This method can be applied to partial models with any size in any posture. Although it is becoming easy to obtain textured 3D models by a 3D scanner, there are few methods for 3D object recognition which take into account both shape and textures of objects. Moreover, it is difficult...
Finding correspondences between feature points is one of the most relevant problems in the whole set of visual tasks. In this paper we address the problem of matching a feature vector (or a matrix) to a given subspace. Given any vector base of such a subspace, we observe a linear combination of its elements with all entries swapped by an unknown permutation. We prove that such a computationally hard...
We review some recent techniques for 3D tracking and occlusion handling for computer vision-based augmented reality. We discuss what their limits for real applications are, and why object recognition techniques are certainly the key to further improvements.
According to the characteristic of ballistic missile target recognition, the reduced-dimensional bispectra methods that are applied in high-resolution range profile recognition are studied, and an improved selected bispectra method based on extended Fisher class separability discriminant measure is proposed. The improved selected bispectra features are evaluated using computer simulation experiment...
Shape matching is one of the more significant research topics in the fields of computer vision, pattern recognition and machine learning. Successful shape matching algorithms/ methods has a high potential for a wide variety of practical applications. In this paper, we present our effort on using linear projection methods for static hand sign recognition in Malaysian sign language. PCA and LPP methods...
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